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Emerging Applications of Artificial Intelligence in Mechanical, Control, Geological, Sensing, and Intelligent Detection Engineering: Integration and Innovations

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 December 2025 | Viewed by 1300

Special Issue Editors

Dr. Changping Li

E-Mail Website
Guest Editor
School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China
Interests: intelligent geological equipment
Dr. Yang Yang

E-Mail Website
Guest Editor
School of Automation, Wuhan University of Technology, Wuhan 430074, China
Interests: fuel cell health management
Prof. Dr. Wenjian Yang

E-Mail
Guest Editor
School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China
Interests: robot and mechanism design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is dedicated to exploring the application of Artificial Intelligence (AI) across diverse engineering domains. While we encourage the use of combined approaches to validate hypotheses through cross-checking, it is essential that AI remains the central focus of all contributions. Authors should demonstrate how AI is integral to their research and how it drives the implementation of innovative ideas. In our view, the involvement of leading scientists is pivotal to fostering a deeper understanding of AI’s potential among readers and newcomers alike. By doing so, this Special Issue aims to uncover groundbreaking insights, such as optimizing complex systems, enhancing decision-making processes, and enabling real-time monitoring—capabilities that would otherwise be difficult to achieve. The scope of this Special Issue spans a wide range of engineering fields, including mechanical, control, geological, sensing, and intelligent detection engineering. These disciplines form the cornerstone of this Special Issue. We particularly encourage submissions that validate preliminary numerical simulations through experimental work, while mathematical explanations should pave the way for new data processing techniques and AI-driven optimization methods. Both in situ applications and laboratory measurements are equally valued, though we anticipate that experimental studies will dominate the published contributions. The lasting message we hope to convey for future generations of engineers and researchers is rooted in AI’s transformative potential. As a powerful, non-intrusive, and adaptable tool, AI enables the analysis of complex systems, the prediction of failures, and the optimization of performance—achievements that are both efficient and sustainable. This Special Issue underscores AI’s critical role in advancing engineering solutions to address pressing challenges in modern society, such as sustainability, automation, and the development of smart infrastructure.

In conclusion, this Special Issue serves as a platform for researchers, engineers, and industry experts to showcase cutting-edge applications of AI in engineering. By fostering interdisciplinary collaboration and innovation, we aim to expand the boundaries of engineering possibilities and ensure that AI remains a cornerstone in shaping the future of technology and industry.

Dr. Changping Li
Dr. Yang Yang
Prof. Dr. Wenjian Yang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • emerging applications
  • optimizing complex systems
  • real-time monitoring
  • data-driven
  • innovative methods

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Published Papers (2 papers)

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Research

31 pages, 9617 KB  
Article
Alleviate Data Scarcity in Remanufacturing: Classifying the Reusability of Parts with Data-Efficient Generative Adversarial Networks (DE-GANs)
by Maximilian Herold, Engjëll Ahmeti, Naga Sai Teja Kolakaleti, Cagatay Odabasi, Jan Koller and Frank Döpper
Appl. Sci. 2025, 15(17), 9833; https://doi.org/10.3390/app15179833 - 8 Sep 2025
Viewed by 524
Abstract
Remanufacturing, a key element of the circular economy, enables products and parts to have new life cycles through a systematic process. Initially, used products (cores) are visually inspected and categorized according to their manufacturer and variant before being disassembled and cleaned. Subsequently, parts [...] Read more.
Remanufacturing, a key element of the circular economy, enables products and parts to have new life cycles through a systematic process. Initially, used products (cores) are visually inspected and categorized according to their manufacturer and variant before being disassembled and cleaned. Subsequently, parts are manually classified as directly reusable, reusable after reconditioning, or recyclable. As demand for remanufactured parts increases, automated classification becomes crucial. However, current Deep Learning (DL) methods, constrained by the scarcity of unique parts, often suffer from insufficient datasets, leading to overfitting. This research explores the effectiveness of Data-Efficient Generative Adversarial Network (DE-GAN) optimization approaches like FastGAN, APA, and InsGen in enhancing dataset diversity. These methods were evaluated against the State-of-the-Art (SOTA) Deep Convolutional Generative Adversarial Network (DCGAN) using metrics such as the Inception Score (IS), Fréchet Inception Distance (FID), and the classification accuracy of ResNet18 models trained with partially synthetic data. FastGAN achieved the lowest FID values among all models and led to a statistically significant improvement in ResNet18 classification accuracy. At a [1:1] real-to-synthetic ratio, the mean accuracy increased from 72% ± 4% (real-data-only) to 87% ± 3% (p < 0.001), and reached 94% ± 3% after hyperparameter optimization. In contrast, synthetic data generated by the SOTA DCGAN did not yield statistically significant improvements. Full article
(This article belongs to the Special Issue Emerging Applications of Artificial Intelligence in Mechanical, Control, Geological, Sensing, and Intelligent Detection Engineering: Integration and Innovations)
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25 pages, 8755 KB  
Article
Acoustic Transmission Characteristics and Model Prediction of Upper and Lower Completion Pipe Strings for Test Production of Natural Gas Hydrate
by Benchong Xu, Haowen Chen, Guoyue Yin, Rulei Qin, Jieyun Gao and Xin He
Appl. Sci. 2025, 15(16), 9174; https://doi.org/10.3390/app15169174 - 20 Aug 2025
Viewed by 404
Abstract
This study adopts numerical simulation methods to explore the acoustic transmission characteristics of pipe strings in the upper and lower completions of a monitoring system for test production of natural gas hydrate. A finite-element simulation model for acoustic transmission in the pipe string [...] Read more.
This study adopts numerical simulation methods to explore the acoustic transmission characteristics of pipe strings in the upper and lower completions of a monitoring system for test production of natural gas hydrate. A finite-element simulation model for acoustic transmission in the pipe string system is established through COMSOL. The sound pressure level attenuation and the sound pressure amplitude ratio are chosen as evaluation indexes. Parametric numerical simulations are carried out to study the effects of the number of tubing cascades and the size of connection joints in the pipe string system on the acoustic transmission characteristics of the pipe string. The Light Gradient Boosting Machine (LightGBM) algorithm is adopted to predict the acoustic transmission characteristic curves of the pipe string. Based on this prediction model, with the maximum transmission distance, maximum sound pressure amplitude ratio, and minimum transmission attenuation as objective functions, the NSGA-II (Non-dominated Sorting Genetic Algorithm-II) optimization algorithm is adopted to obtain the optimal combinations of the pipe string system structure and the transmission frequency. The findings show that within the range of 20–2000 Hz, when the acoustic wave propagates in the column system, the amplitude attenuation caused by structural damping is positively correlated with the transmission distance, and the high-frequency acoustic wave attenuates faster. When the frequency exceeds 500 Hz, the sound pressure amplitude ratio is lower than 0.4, and the attenuation is stabilized at 90% above 1500 Hz. The thickness of the joints has a weak impact on the transmission, while an increase in length raises the characteristic frequency but exacerbates sound pressure attenuation. The LightGBM algorithm has a high prediction accuracy, reaching up to 88.54% and 84.82%, respectively. The optimal parameter combinations (n, hkg, lkg, freq) optimized by NSGA-II provide an optimization scheme for the structure and frequency of acoustic transmission in down-hole pipe strings. Full article
(This article belongs to the Special Issue Emerging Applications of Artificial Intelligence in Mechanical, Control, Geological, Sensing, and Intelligent Detection Engineering: Integration and Innovations)
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